Fast Pedestrian Detection With Attention-Enhanced Multi-Scale RPN and Soft-Cascaded Decision Trees

Fast Pedestrian Detection by Cascaded Random Forest with Dominant Orientation Templates

In this paper, we present a new pedestrian detection method combining Random Forest and Dominant Orientation Templates(DOT) to achieve state-of-the-art accuracy and, more importantly, to accelerate run-time speed. DOT can be considered as a binary version of Histogram of Oriented Gradients(HOG) and therefore provides time-efficient properties. However, since discarding magnitude information, it...

Multi-scale Singularity Trees: Soft-Linked Scale-Space Hierarchies

Fast Human Detection with Cascaded Ensembles

Detecting people in images is a challenging task because of the variability in clothing and illumination conditions, and the wide range of poses that people can adopt. To discriminate the human shape clearly, Dalal and Triggs [1] proposed a gradient based, robust feature set that yielded excellent detection results. This method computes locally normalized gradient orientation histograms over bl...

Bagging Soft Decision Trees

The decision tree is one of the earliest predictive models in machine learning. In the soft decision tree, based on the hierarchical mixture of experts model, internal binary nodes take soft decisions and choose both children with probabilities given by a sigmoid gating function. Hence for an input, all the paths to all the leaves are traversed and all those leaves contribute to the final decis...

Optimal Cascaded Hierarchies of Linguistic Decision Trees for Decision Making

For multiple attribute decision making, the underlying relationship between attributes and classification, decision or utility variable is often highly uncertain and imprecise. This requires an integrated treatment of uncertainty and fuzziness when modeling the propagation of information from low-level attributes to high-level decision variables. One of the main drawbacks to fuzzy modeling of s...